Custom built ethnic-specific dataset from SAP tests for optic pathway disease diagnosis: baseline approach using graph convolutional networks (2021)
Automated interpretation and diagnostic classification of standard automated perimetry (SAP) plots has been researched using machine learning techniques, and multiple public datasets has been proposed. Therefore, our contribution is a more complete database than the already published ones, and we propose an analysis of state of the art techniques over this dataset. This ethnic-specific database, contains 66 subjects from Colombia ranging from 17-79 years old, differentiating them in two major classes: visual field (VF) of optic pathway pathological patients, and healthy eyes. Moreover, we propose, in particular, a flexible representation using Graph Convolutional Networks that combines the three SAP plots, VF indices (MD, PSD, VFI) and patient¿s data (age, pupil diameter), into a simple graph to pursue improved classification performance compared to using any single SAP plot and a variety of CNN architectures. We achieved an accuracy of 1.0 in both validation and test sets. Performance was superior compared to using other Machine Learning (ML) methods tested.